1. Automatic Image Segmentation of Lesions in Multispectral Brain
Graduate School of Industrial Engineering and Management
National Yunlin University of Science & Technology Master Thesis
Automatic Image Segmentation of Lesions in Multispectral Brain
Magnetic Resonance Imaging
Student：Yu-Ting Hsu
Advisor：JaChih Fu, Ph.D.

2. Outline Introduction Literature Review Research Method
Expected Results 2

3. Outline Introduction Literature Review Research Method
Expected Results
Outline
Introduction
Research Background and Motive
Research Purpose 
Literature Review
Research Method
Expected Results 3

4. Research Background and Motive (1/2)
Introduction
Literature Review
Research Method
Expected Results
Research Background and Motive (1/2)
Issues of brain tumor are concerned in recent years.
Magnetic Resonance Imaging, MRI
Good vision between different soft tissues
Does not use ionizing radiation
Brain Tissues
Grey Matter, GM
White Matter, WM
Cerebrospinal Fluid, CSF
Tumor
Edema 4

5. Research Background and Motive (2/2)
Introduction
Literature Review
Research Method
Expected Results
Research Background and Motive (2/2)
Type of MRI
T1-weighted Magnetic Resonance Imaging
T2-weighted Magnetic Resonance Imaging
Flair Magnetic Resonance Imaging
Measure Method of Lesion Regions
Manual
Semi-automatic segmentation algorithm
T1 MRI
T2 MRI
Flair MRI 5

6. Research Purpose Multispectral MRI Analysis
Introduction
Literature Review
Research Method
Expected Results
Research Purpose
Multispectral MRI Analysis
Develop an automatic segmentation algorithm
Segment lesion regions
Tumor
Edema
Original Imaging
Image Preprocessing
Automatic Image Segmentation
Performance Evaluation 6

7. Outline Introduction Literature Review Research Method
Expected Results
Outline
Introduction
Literature Review
Single-spectral Image Segmentation Algorithm
Heuristic Algorithms
Multi-spectral Image Segmentation Algorithm
Research Method
Expected Results 7

8. Single-spectral Image Segmentation Algorithm(1/2)
Introduction
Literature Review
Research Method
Expected Results
Single-spectral Image Segmentation Algorithm(1/2)
Active Contours using Level Sets, ACLS
Chan (2001) propose Active Contours using Level Sets to detect objects in a given image.
Detect boundary energy by , expect is minimized.
Original
image
Initial
contour
Boundary
detection
Lesion regions 8

9. Single-spectral Image Segmentation Algorithm(2/2)
Introduction
Literature Review
Research Method
Expected Results
Single-spectral Image Segmentation Algorithm(2/2)
Energy function of ACLS：
Inside Energy
Outside Energy
λ：Length weighted coefficient
ν：Area weighted coefficient
μ：Inside energy coefficient
g : Gradient
ε : Smoothing parameter
: Length weighted formula
Influence Parameters
: Area weighted formula
: Inside energy formula 9

10. ACLS Application Author Year Application Paul et al. 2006
Introduction
Literature Review
Research Method
Expected Results
ACLS Application
Author
Year
Application
Paul et al.
2006
Applied ACLS for caudate nucleus and lateral ventricle segmentation in child MRI.
Taheri et al.
2010
A threshold-based scheme that uses level sets for 3D tumor segmentation.
Hwang et al.
2011
Presented the active contour model with shape prior for extracting the cerebellum from T1-weighted brain MR images.
Sachdeva et al.
2012
Content-based active contour uses both intensity and texture information present within the active contour to overcome oversegmentation in an image. 10

11. Heuristic Algorithms(1/2)
Introduction
Literature Review
Research Method
Expected Results
Heuristic Algorithms(1/2)
Methods of Parameters Optimization
Taguchi Methods、Genetic Algorithms、Grid Search
Algorithms
Advantages
Disadvantages
Taguchi Methods
Get optimal factor fitting by less experiences
Lack systematize
System lacks efficiency
May not be the global optimal solution
Genetic Algorithms
Distributed search
Can search large range
Coding may encounter difficulties
High cost
Large calculating time
Grid Search Algorithms
Good quality
If range of parameters is too large, it needs time to calculate optimal parameters. 11

12. Heuristic Algorithms(2/2)
Introduction
Literature Review
Research Method
Expected Results
Heuristic Algorithms(2/2)
Grid Search Algorithm
Multilevel
Process Simultaneously on different computers
Decide Parameter Range
Segment Equidistant Grids
β =0~20
Calculate Object Function of Each Grids
β=5~15
Select Optimal Solution
Segment Grids
Yes
α=10~20 No
Optimal Solution
α=0~20 12

13. Grid Search Application
Introduction
Literature Review
Research Method
Expected Results
Grid Search Application
Author
Year
Application
Mu et al.
2005
Use grid search to find optimal parameters of V-SVM. Applied v-SVM learning to detect breast cancer.
Huang et al.
2008
Use grid search to find optimal parameters of SVM. Let SVM render a diagnosis between the breast cancer.
Li et al.
2010
DCE data sensitivities to various parameters were tested using comparisons that effected parametric grid searches of error surfaces.
Lin et al.
Use grid search to find optimal parameters of SVM. 13

14. Multi-spectral Image Segmentation Algorithm(1/3)
Introduction
Literature Review
Research Method
Expected Results
Multi-spectral Image Segmentation Algorithm(1/3)
Type of MRI
T1、T2、Flair
𝑥 1 = 𝑥 11 , …𝑥 𝑖1 … 𝑥 𝑛1
𝑥 2 = 𝑥 12 , …𝑥 𝑖2 … 𝑥 𝑛2
𝑥 3 = 𝑥 13 , …𝑥 𝑖3 … 𝑥 𝑛3
T1 weighted
T2 weighted
Flair
Conception of Multi-spectral Images： 𝑋= 𝑥 1 , 𝑥 2 , 𝑥 3 14

15. Multi-spectral Image Segmentation Algorithm(2/3)
Introduction
Literature Review
Research Method
Expected Results
Multi-spectral Image Segmentation Algorithm(2/3)
Support Vector Machine, SVM
A binary classifier ＋1 －1
, Objective Function
w：Normal vectors of hyperplane
b ：Offset
𝑑 + ：the shortest distance of +1 training data to separating hyperplane
𝑑 − ：the shortest distance of -1 training data to separating hyperplane
Margin：Maximum-margin hyperplane 15

16. Multi-spectral Image Segmentation Algorithm(3/3)
Introduction
Literature Review
Research Method
Expected Results
Multi-spectral Image Segmentation Algorithm(3/3)
One-against-all Method, OAA
One of methods for multi-class SVM
Each of the SVMs separates a single class from all remaining classes (Cortes and Vapnik, 1995)
𝑦 𝑖 =+1：1
𝑦 𝑖 =−1：2 , 3 , … , k
𝑦 𝑖 =+1：2
𝑦 𝑖 =+1：𝑘
𝑦 𝑖 =−1：1 , 3 , … , k
𝑦 𝑖 =−1：1 , 2 , … , k−1
max
Class of 𝑥=argmax 𝑖=1,2,…,𝑘 ( 𝑤 𝑖 ∙𝜑 𝑥 + 𝑏 𝑖 ) 𝑖 𝑥 … 16

17. SVM Application Author Year Application Miranda et al. 2005
Introduction
Literature Review
Research Method
Expected Results
SVM Application
Author
Year
Application
Miranda et al.
2005
Applied SVM to perform multivariate classification of brain states from whole fMRI volumes without prior selection of spatial features.
Chaplot et al.
2006
Support vector machine classifies MR brain images as either normal or abnormal.
Lao et al.
2008
SVM classifier is first trained on expert-defined white matter lesions from multi-parametric MRI sequences.
Yamamoto et al.
2010
Develop a computerized method which consisted of a rule-based method, a level set method, and SVM for detection of multiple sclerosis (MS) lesions in brain MRI.
multiple sclerosis (MS) 多發性硬化證 17

18. Outline Introduction Literature Review Research Method
Expected Results
Outline
Introduction
Literature Review
Research Method
Research Flowchart
Image Preprocessing
Parameter Definition
Grid Search Heuristic Algorithms
ACLS Segmentation Algorithms
Multi-spectral Segmentation Algorithms
Performance Evaluation
Expected Results 18

19. Research Flowchart Introduction Literature Review Research Method
Expected Results
Research Flowchart 19

20. Image Preprocessing Image Resampling Increasing the size of an image
Introduction
Literature Review
Research Method
Expected Results
Image Preprocessing
Image Resampling
Increasing the size of an image
512*512
256*256
Original Image
Resampling Image
Resize 20

21. Cut the image without lesions
Introduction
Literature Review
Research Method
Expected Results
Image Preprocessing
Select initial contour
Differences of pixel in T2 and Flair
Select coordinates of this boundary be initial contour
MR Images
Cut the image without lesions
Subtract pixels
Lesion Region
Boundary of lesion 21

22. Parameters Definition
Introduction
Literature Review
Research Method
Expected Results
Parameters Definition
Influence parameters of ACLS
μ：Inside energy coefficient , ratio of curve moving speed
λ：Length weighted coefficient , ratio of curve twist
ν：Area weighted coefficient , ratio of curve extension
ε：Smoothing parameter , ratio of curve smoothness 22

23. Grid Search Heuristic Algorithms
Introduction
Literature Review
Research Method
Expected Results
Grid Search Heuristic Algorithms
Find optimal parameters to make objective function maximization.
Number of parameters：4 ( μ 、λ、ν、ε )
Generate Initial Parameters
Calculate Object Function of Grids
Search Next Grid
Stop Conditions No
Yes
Optimal Parameters 23

24. ACLS Segmentation Algorithms
Introduction
Literature Review
Research Method
Expected Results
ACLS Segmentation Algorithms
MR Images
Initial Contour
Boundary Detection
Lesion Regions 24

25. Multi-spectral Segmentation Algorithms
Introduction
Literature Review
Research Method
Expected Results
Multi-spectral Segmentation Algorithms
MR Images
Select Training Samples
SVM-OAA
Segmentation Results 25

26. Performance Evaluation
Introduction
Literature Review
Research Method
Expected Results
Performance Evaluation
Jaccard Similarity
Jaccard Similarity Formulation :
, 0 < J ( 𝑆 1 , 𝑆 2 ) < 1
Results of
Automatic Segmentation
Golden Standard
本論文的品質特性是將手動切割作為Golden Standard，與自動切割所得進行Jaccard Similarity運算
假設S1是手動切割，s2是自動切割，交集之部份則為正確分割之區域。
Jaccard Similarity公式則是S1與S2交集/ S1與S2聯集 FA 26

27. Outline Introduction Literature Review Research Method
Expected Results
Outline
Introduction
Literature Review
Research Method
Expected Results 27

28. Introduction
Literature Review
Research Method
Expected Results
Expected Results
Automatic segmentation of lesions in MRI using ACLS algorithm
Automatic segmentation of lesions in MRI using multispectral segmentation algorithm
Performance evaluation
Select the algorithm which has better performance 28

29. End 29